Selecting an obstetrician or midwife and birth center or hospital is arguably one of the most important decisions that a pregnant woman makes. This choice will determine many aspects of a woman’s pregnancy journey, including the likelihood that she delivers via C-section. To understand how women choose their obstetric provider and their delivery facility, Ovia Health has teamed up with Ariadne Labs to survey women and help shed light on this important decision-making process.
C-sections in America
Few would debate that the United States is experiencing a C-section epidemic. One out of every three babies is born via C-section, despite the fact that 1) research shows that most pregnant women prefer and plan for vaginal delivery and 2) the World Health Organization (WHO) has argued that 10-15% is the optimal cesarean rate, placing the US at double or even triple the optimal rate. As the most common major surgery performed in the United States, C-sections are responsible for 20,000 surgical complications and infections annually, and account for over $5 billion in excess medical spend each year. Although C-sections can be life saving interventions, they still pose significant risks to both mother and child. C-section rates have risen at alarming rates across the United States, and many people, both inside and outside the medical community, are dedicated to uncovering and reversing the root cause of this trend.
Hospital as a risk factor
A major risk factor for delivering via C-section is hospital choice. Groups such as Consumer Reports have uncovered disturbingly high differences between C-section rates at hospitals just miles away from each other. There is evidence that there is a 10-fold variation in the cesarean delivery rate across hospitals in the United States — ranging from 7% to almost 70%. This variance cannot be accounted for by known risk factors such as age, ethnicity, and weight, leaving researchers to ponder what is it about certain hospitals that lead to such large discrepancies in the number of C-sections performed.
We know that hospital culture and management practices influence delivery decisions. Labor and delivery floor management practices, availability of midwives within a hospital setting, obstetric provider preference, and scheduling conflicts have all been shown to play a role in a patient’s likelihood of delivering via C-section. Most obstetric providers no longer encourage elective C-sections. However, a variety of procedures commonly used in routine obstetric practice, such as continuous electronic fetal monitoring, have been shown to increase provider reliance on C-sections.
Despite the established importance of hospital C-section rates and the emphasis that the medical community has placed on them, recent research shows that most women do not consider hospital C-section rates when making delivery decisions. When push came to shove, this research found that women were unwilling to travel to a hospital further away even if it had a 20% lower C-section rate.
We know that women do not consider C-section rates when making a choice about their delivery care. Yet little else is known about how women decide on a hospital or obstetric provider for their pregnancy. This gap in knowledge prevents researchers from tailoring interventions to influence hospital choice effectively, which is why a collaborative team from Ovia Health and Ariadne Labs surveyed over 9,000 users of the Ovia Pregnancy app to understand the factors that most influence the hospital and obstetric provider decision-making process.
Which comes first, hospital or provider?
The first step in this process is selecting either a hospital or an obstetric provider. As expected, the majority of our survey respondents chose their provider before their hospital — 71% to 25%. Respondents who chose their provider first were more likely to choose earlier — 53% said they already had a provider prior to trying to conceive. In contrast, 40% of respondents who chose their hospital first made this decision as soon as they found out they were pregnant. This suggests some difference between women who choose their hospital first and women who choose their provider first.
How is the decision made?
Unsurprisingly, insurance coverage and location strongly influence a woman’s decision of either provider or hospital. Over 78% of our participants said insurance was a factor in their obstetric provider choice, compared to 40% saying insurance was a factor in their hospital choice. Over 55% of respondents identified location as a factor in their provider choice, while close to 48% said that location was a factor in hospital choice as well. Our learnings in this regard are not novel, yet tell us that cost and convenience cannot be overlooked by those seeking to understand this decision-making process.
Yet when women were asked to choose only the most important factor that influenced their hospital or provider decision, women gave the most weight to recommendations from family and friends in both deciding where to deliver and who will deliver their babies. As it turns out, women are engaging in pretty rigorous qualitative analysis to come to a final decision about their prenatal and delivery care.
Unfortunately, this thoughtful decision-making process does not incorporate hospital C-section rates. In fact, less than 1% of respondents rated C-section rates as the most important factor in hospital choice. Yet, almost 1 in 5 women who had not chosen their provider or hospital said they would consider C-section rates in their decision-making process. This begs the question, why aren’t they considering them in practice?
Could it be that women cannot access or interpret these C-section rates? There is no unified database of accurate C-section rates due to the differing data-sharing practices that exist at the state-level. Some states such as California share C-section data about hospitals on their websites, whereas other states’ privacy laws prevent these data from being shared with the public under any circumstances. This creates a confusing landscape for women to traverse when considering hospital quality metrics. Furthermore, recent research tells us that most women do actually care about hospital quality metrics, they just don’t know how to access or interpret that data effectively.
The good news is that most pregnant women already engage in their own fairly rigorous research process before making a hospital or provider decision. Our survey participants reported using insurance websites to search for, and more importantly, compare in-network providers and hospitals. Peer level data is also relevant to these women — many respondents reported researching community reviews, using HealthGrades to access provider ratings, and some even turned to Yelp.
We find these results encouraging. Women already care about recommendations, ratings, and reviews, so incorporating C-section rates into the decision-making process should be possible. Our results indicate that the medical community needs to do a better job of translating hospital quality data so that it is easier to understand and access. We hope that when hospital quality metrics become widely accessible and easy to use, women will start leveraging these data to guide their choices about how and where to deliver. Ariadne Labs and Ovia Health aim to be a forces of change in this landscape, providing transparent information about doctors, midwives, and hospitals to Ovia Health’s millions of users in a way that is useful and can help women make an informed decisions.
There is no one factor that is the critical game-changer when women make decisions about where and who should deliver their babies. Instead, a combination of factors leads to the ultimate decision. It is our hope that as hospital quality metrics, especially C-section rates, become more accessible and easier to interpret, women will start using these data to make their most important pregnancy decision.
Delivery Decisions Survey Methodology
The provider and hospital decisions survey aimed to gather greater insight into how pregnant women make decisions about the care they receive from the healthcare system throughout their pregnancies. We surveyed over 9,000 pregnant users of the Ovia Pregnancy app, a mobile app that enables women to track their pregnancies and learn more about their fetus’s development. The majority of respondents were between the ages of 20-29 (50%) and 30-49 (46%). Most participants identified as White (75%), Hispanic (9%), or Black or African American (7%). Education and income levels were evenly distributed.
Erin Landau is with Ovia Health and Dr. Reena Aggarwal is a researcher with Ariadne Labs
Great stats. Thanks for sharing. For me, the main factor of choosing between a provider or hospital was insurance coverage and location. But not to forget about cost and convenience. Plus, I really cared about the recommendations, ratings, reviews, etc. HealthGrades – helped a lot in accessing provider ratings. I’m sure conscious women will make huge research even before she got pregnant to ensure she and her future baby receive quality care.
Very well-written. A great and an interesting read and good stat. Providers need to retain their patients within the network and the only way to retain them is by providing quality care. Today with new innovative IT solution providers are able to achieve quality healthcare and better patient outcomes. Patient referral management solution will be of great help to retain patient within your network and at the same time provide quality care.
Thank you for this interesting post. I am one of the few willing to choose the hospital first, I guess, one with a NICU, 60 minute drive from my home. Then I chose a provider based on word of mouth. She had a breakdown one week before I delivered so the OB who delivered me was someone I had never met. He ended up delivering the next three babies after that. As a pediatrician, the most important thing was if anything went wrong, i could be with my newborn every step of the way, even in the event of demise. The hospital did have a low C/S rate and that was a bonus.
As a means to focus our collective energies to solve this problem for half of the citizens within the Common Good of each community, I offer a definition of SOCIAL CAPITAL. The key word is “generational.”
Social Capital may be defined as:
.the attributes of TRUST, COOPERATION and RECIPROCITY,
.as expressed during a community’s Social Discourse,
.that its citizens apply more commonly for resolving the Social Dilemmas
.they encounter while participating in their community’s Civil Life
.when ‘generational’ Caring Relationships are prevalent
.within and between the Human Ecologic networks of the community’s citizens,
.especially the ‘neighborhood network’ of each citizen’s Family.
It is occasionally characterized as the GLUE for maintaining a community’s commitment to its COMMON GOOD.
There have been three, multi-year, data sets for State by State maternal mortality ratios that have been remotely published: 1987-1996, 2001-2006 and 2005-2014. Each data-set can be rank ordered from lowest to highest and divided into 6 separate Clusters of States. The lowest two clusters of the three data sets had an average MMR of 4.12, 5.59 and 9.58 respectively. Similarly, the highest two Clusters had an average MMR of 8.73,16.43 and 24.88 respectively. An MMR reflects deaths per 100,000 live births using the WHO definition.
The state by state data has been plagued by certain state by state alternate definitions and data collection problems. All of which have been used to prevent a nationally coordinated focus on the real issues. The basic underlying issues remain fundamentally unattended, especially the pockets of poverty that exist in some states more than others.
For all three of the Data-Sets, there were 7 states that were in each of the 2 best Clusters, and similarly there were 7 States that were in each of the 2 worst clusters. The best States were (rank ordered, lowest first) Massachusetts, Alaska, Oregon, Rhode Island, Iowa, Connecticut and Minnesota. The worst States were (rank ordered, worst last) New York, Florida, New Mexico, Louisiana, Maryland, Mississippi and Georgia.
There was only one state that was in the middle two Clusters for all three data sets: Pennsylvania.
I have spent nearly 10 years now trying to sort out the reasons for all of this. The end analysis is grim. Out of nearly 1000 maternal deaths a year (out of 4 million live births), at least 500 deaths occurred because the mother lived in the wrong nation before her pregnancy started. In comparison to the other 34 OECD nation’s, we would need to reduce our nation’s maternal mortality by >70% to rank among the lowest 10 of these developed nations. We are the ONLY developed nation with a worsening maternal mortality ratio for 25 years in a row (SEE WHO/UNICEF report for 2015).
It would be an error for me to ignore a heroic effort in many locations to fix this problem. There are 8 states that have significantly improved their ranking from the first to the third data-sets by two clusters or more: Virginia, Maine, Nevada, Colorado, California, Vermont, Alabama, and North Carolina. And another 8 States improved by one cluster from the first to third Data set: Utah, Indiana, Wyoming, Idaho, Arkansas, Oklahoma, New Jersey and Michigan. So, nearly 50% of the States are making a supreme effort, and similarly the other States are struggling.
Over-all, it is likely that there are fundamental socio-economic factors, i.e., poverty and worsening social mobility, that are connected with our nation’s worsening MMR. This is probably related to a host of other problems such as the excess level of health spending and our nation’s decreasing longevity for the last two years. Unfortunately, I perceive that the cost and quality problems are basically unaffected by our nation’s current healthcare reform strategy. It is all best acknowledged as a “Tragedy of the Commons.” We have only to ask our citizens in Alaska: What’s the answer? My own answer is that they have the Social Capital in place to understand and commit to the survival of each citizen no matter what their ecologic niche might be, year around. We have only to mimic their priorities, community by community. So far, Paradigm Paralysis reigns supreme!